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applied sciences Article Development of Auto-Seeding System Using Image Processing Technology in the Sapphire Crystal Growth Process via the Kyropoulos Method 1 1 , 2 Churl Min Kim , Sung Ryul Kim * and Jung Hwan Ahn Precision Manufacturing & Control R&D Group, Korea Institute of Industrial Technology, 1,30, Gwahaksandan 1-ro 60 beon-gil, Gangseo-gu, Busan 46742, Korea; chmikim@kitech.re.kr School of Mechanical Engineering, Pusan National University, 2, Busan daehak-ro 63 beon-gil, Geumjeong-gu, Busan 46241, Korea; jhwahn@pusan.ac.kr * Correspondence: sungrkim@kitech.re.kr; Tel.: +82-051-974-9259 Academic Editor: Giorgio Biasiol Received: 2 February 2017; Accepted: 5 April 2017; Published: 7 April 2017 Abstract: The Kyropoulos (Ky) and Czochralski (Cz) methods of crystal growth are used for large-diameter single crystals. The seeding process in these methods must induce initial crystallization by initiating contact between the seed crystals and the surface of the melted material. In the Ky and Cz methods, the seeding process lays the foundation for ingot growth during the entire growth process. When any defect occurs in this process, it is likely to spread to the entire ingot. In this paper, a vision system was constructed for auto seeding and for observing the surface of the melt in the Ky method. An algorithm was developed to detect the time when the internal convection of the melt is stabilized by observing the shape of the spoke pattern on the melt material surface. Then, the vision system and algorithm were applied to the growth furnace, and the possibility of process automation was examined for sapphire growth. To confirm that the convection of the melt was stabilized, the position of the island (i.e., the center of a spoke pattern) was detected using the vision system and image processing. When the observed coordinates for the center of the island were compared with the coordinates detected from the image processing algorithm, there was an average error of 1.87 mm (based on an image with 1024 768 pixels). Keywords: sapphire; single crystal growth; Kyropoulos method; auto-seeding; image processing; spoke pattern 1. Introduction Single-crystal sapphire is a material that has high hardness, excellent chemical stability, and optical transparency in a wide range of wavelengths. Due to these advantages, it is widely used in various industries including engineering, military supply, aviation, optics, and healthcare. At present, large quantities of sapphire ingots are being produced due to the prevalence of light-emitting diode back light units (LED BLUs), which use sapphire substrates, and the demand for this material is increasing rapidly [1]. Accordingly, many studies are being conducted on the production of large-diameter single crystals because they provide greater benefits than small-diameter single crystals in terms of productivity and market price. For the growth of large-diameter single-crystal sapphire, the Kyropoulos (Ky) and Czochralski (Cz) methods are predominately used. The Cz method is a wide spread single crystal growth technique in which alumina in melted in a crucible and the seed is pulled up; the seed is simultaneously rotated after it contacts the surface of the molten metal [2]. The Ky method is mainly used for the single crystal growth of large-diameter sapphire. Though its basic growth furnace is similar to that of the Appl. Sci. 2017, 7, 371; doi:10.3390/app7040371 www.mdpi.com/journal/applsci Appl. Sci. 2017, 7, 371 2 of 14 Cz method, the seed is not rotated after it contacts the molten alumina, but the heater temperature is slowly lowered so that the single crystal grows downward from the seed [3]. Many studies have been conducted on the Ky and Cz sapphire growth methods. Timofeev et al. [4,5] conducted a study on 3D simulations of the sapphire growth method and the effect of heating conditions on melt convection in a Ky furnace. Chen [6] and Demina [3] researched the effects of temperature conditions on ingot shape during growth in the Ky method. Nouri [7] investigated the interaction between the heat zone and the internal melt in the seeding process of the Ky method. These researchers focused on analyzing the growth shape of single crystal sapphire by numerical analysis, paying attention to heater arrangement and power. The following studies are also related to growth process monitoring. Kozik and Nezhevenko [8] investigated methods for measuring the diameter of ingots during the single crystal growth of sapphire using vision system. Xiang et al. [9] proposed measuring the melt level in Cz crystal pullers using an image-based laser-triangulation measurement system. In addition, Winkler [10,11] investigated the automation of the growth furnace in the Cz method, but this research deals with partial automation after the seeding process. As in previous papers, it is difficult to find an entirely automated process for single crystal growth. This is because the optimization of the process conditions for seeding has not been established. Most studies that used Ky and Cz methods stated that seeding was performed when the temperature field of the melt material in the growth furnace was stabilized [12–14]. Until now, the seeding process in ingot growth, which has great influence on the quality of single crystal sapphire, has depended on the experience and technique of skilled workers. The seeding process lays the foundation for ingot growth in both Ky and Cz methods, and when any defect occurs in this process, it is likely to spread to the entire ingot. So, the growth process must be standardized and stabilized to grow a single crystal sapphire of good quality. The aim of this research was to establish and optimize the conditions of the seeding process in the Ky method. The vision system and algorithm were developed to detect the exact time when the internal convection of the melt is stabilized by observing the shape of the spoke pattern on the melt surface. The possibility of sapphire process automation was also examined and applied to the growth furnace. 2. Principles 2.1. Detection of Auto Seeding Point in Ingot Growth In single crystal sapphire growth methods that use a cylindrical crucible, such as Ky and Cz, a stream line generated from the edge to the center of the crucible on the surface of the melt when convection occurs (after the internal alumina is fully molten). This melt material flows because of the temperature gradient caused by the heater of the outer crucible. Generally, the flow rises on the surface of the crucible wall and moves to the center of the crucible before descending. As convection progresses, multiple stream lines enter through the center of the crucible, forming a spoke pattern [4,15]. The center of the spoke pattern where the melt flow descends is called the island. This pattern on the surface of the melt is also measured in sapphire growth methods that apply heat to a cylindrical crucible in axial symmetry [16–18]. Furthermore, this spoke pattern is measured in natural convection phenomena [19]. If the temperature field of the melt in the growth furnace is stabilized, then convection in the growth furnace is stabilized, and the island is located in the center of the crucible during growth as shown in Figure 1. The seeding process in the Ky method succeeds only when the top-down melting movement is dominant at the seeding point on the melt surface. Upward melt flow at the seeding point cause the seed to melt [20]. Therefore, if the position of the island on the surface of the melt can be measured automatically, the time when the internal convection of the melt is stabilized and when the seeding process is initiated can be detected Appl. Sci. 2017, 7, 371 3 of 14 Appl. Sci. 2017, 7, 371 3 of 14 Figure 1. Spoke patterns on the surface of the melt according to temperature distribution in Figure 1. Spoke patterns on the surface of the melt according to temperature distribution in the crucible. the crucible. 2.2. Image Processing Algorithm 2.2. Image Processing Algorithm The sapphire growth process of the Ky method consists of the following stages: the heating stage, The sapphire growth process of the Ky method consists of the following stages: the heating stage, in which alumina is put into the crucible and heated; the melting stage, in which the alumina is molten; in which alumina is put into the crucible and heated; the melting stage, in which the alumina is the seed preparation stage, in which the temperatures of melted alumina, stream lines, and island molten; the seed preparation stage, in which the temperatures of melted alumina, stream lines, and position are observed to prepare for seeding; and the seed descending stage. Seed cleaning occurs island position are observed to prepare for seeding; and the seed descending stage. Seed cleaning through the first contact between the seed and the melt, and the second contact between the seed and occurs through the first contact between the seed and the melt, and the second contact between the the melt for sapphire growth. seed and the The pr melt for ocess observed sapphire g in this rowth. study is the seed preparation stage. In this process, the worker generally The process monitors observed in t the surfaceh of is stud the melt y is the seed pr in the growth furnace eparation stage. In this process, the worke through a view point with the naked r eye. Convection becomes activated from the heater around the crucible after the internal alumina is generally monitors the surface of the melt in the growth furnace through a view point with the naked fully molten. At this time, stream lines begin to form in areas where the temperature is lower than the eye. Convection becomes activated from the heater around the crucible after the internal alumina is surrounding areas on the surface of the melt, and a spoke pattern can be observed as a result. When fully molten. At this time, stream lines begin to form in areas where the temperature is lower than the melt material temperature inside the growth furnace is stabilized, the convection of the alumina the surrounding areas on the surface of the melt, and a spoke pattern can be observed as a result. melt is also stabilized, and the island, is located at the center of the growth furnace crucible. Then, When the melt material temperature inside the growth furnace is stabilized, the convection of the when the island size grows to 10–20 mm, seeding begins. To detect the seeding point accurately, a alumina melt is also stabilized, and the island, is located at the center of the growth furnace crucible. technique is required for automatic detection of island size and the time when the center of the island Then, when the island size grows to 10–20 mm, seeding begins. To detect the seeding point coincides with the center of the crucible. accurately, a technique is required for automatic detection of island size and the time when the center To measure the melt surface inside the growth furnace using an image, a charge coupled device of the island coincides with the center of the crucible. (CCD) camera is installed at a view point on the top of the growth furnace. However, due to the To measure the melt surface inside the growth furnace using an image, a charge coupled device location and size of the view point, only a portion of the actual melt material surface can be measured (CCD) camera is installed at a view point on the top of the growth furnace. However, due to the as shown in Figure 2. location and size of the view point, only a portion of the actual melt material surface can be measured Therefore, an algorithm was developed to measure the shape of the spoke pattern from a limited as shown in Figure 2. field of vision, to detect stream lines using various data obtained from the surface of the melt, and to Therefore, an algorithm was developed to measure the shape of the spoke pattern from a limited measure the island, or the intersection point of the stream lines field of vision, to detect stream lines using various data obtained from the surface of the melt, and to The image processing procedure is divided into the image preprocessing stage, in which the measure the island, or the intersection point of the stream lines collected images are binarized, and the image processing stage in which the center point of the island is The image pr traced throughocessin binarized g proc data, edure as shown is div inided into the Figure 3. image preprocessing stage, in which the collected images are binarized, and the image processing stage in which the center point of the island is traced through binarized data, as shown in Figure 3. Image preprocessing is not only important for acquiring accurate stream-line data, but is also related to the accuracy of the measured data. The luminance plane is extracted to obtain information related to the brightness of the melt surface, which is obtained from the CCD camera images. The extracted image goes through a binarization process that distinguishes particles (stream lines) from non-particles (background). Global binarization, which is one of the image binarization methods that uses threshold values, provides the advantage of minimal computation. However, both the surface temperature distribution and brightness of the extracted image are irregular. It is difficult to obtain accurate stream-line data through global binarization alone. Hence, the Niblack binarization Appl. Sci. 2017, 7, 371 4 of 14 Appl. Sci. 2017, 7, 371 4 of 14 algorithm is used to overcome this condition. Niblack binarization takes threshold values by region and then performs binarization. The Niblack binarization equation is shown below: algorithm is used to overcome this condition. Niblack binarization takes threshold values by region (1) and then performs binarization. The Niblac , k b inarization equation is shown belo , ∙ , w: In this equation, m(x, y) and s(x, y) denote the average and standard deviation in a regional , , ∙ , (1) window, and k denotes the user defined variable for binarization. Niblack binarization determines In this equation, m(x, y) and s(x, y) denote the average and standard deviation in a regional the critical value using the average and standard deviation of pixel values inside the regional window, window, and k denotes the user defined variable for binarization. Niblack binarization determines and the parts above the threshold value are extracted by comparing the critical value with the original the critical value using the average and standard deviation of pixel values inside the regional window, image; these parts are then binarized. and the parts above the threshold value are extracted by comparing the critical value with the original In this study, the deviation value of 0.2 was used in a window with 32 × 32 pixels. Because the image; these parts are then binarized. parts detected on the image are stream lines with low temperatures, dark objects were extracted. In this study, the deviation value of 0.2 was used in a window with 32 × 32 pixels. Because the After binarization, a mask method is used to delete data associated with the rod part, in which parts detected on the image are stream lines with low temperatures, dark objects were extracted. the seed is mounted, and the outer parts, which are not necessary for the measurement of stream After binarization, a mask method is used to delete data associated with the rod part, in which lines on the surface of the melt. The region of interest is extracted from the original image using the the seed is mounted, and the outer parts, which are not necessary for the measurement of stream Appl. Sci. 2017, 7, 371 4 of 14 mask method after binarization because the boundary line in the image may appear as data if lines on the surface of the melt. The region of interest is extracted from the original image using the binarization is performed after extraction. mask method after binarization because the boundary line in the image may appear as data if binarization is performed after extraction. Figure 2. Comparison of the spoke pattern and observed pattern on the surface of the melt. Figure 2. Comparison of the spoke pattern and observed pattern on the surface of the melt. Figure 2. Comparison of the spoke pattern and observed pattern on the surface of the melt. Figure 3. Image processing procedure to find the center point of the spoke pattern. Figure 3. Image processing procedure to find the center point of the spoke pattern. Figure 3. Image processing procedure to find the center point of the spoke pattern. Image preprocessing is not only important for acquiring accurate stream-line data, but is also related to the accuracy of the measured data. The luminance plane is extracted to obtain information related to the brightness of the melt surface, which is obtained from the CCD camera images. The extracted image goes through a binarization process that distinguishes particles (stream lines) from non-particles (background). Global binarization, which is one of the image binarization methods that uses threshold values, provides the advantage of minimal computation. However, both the surface temperature distribution and brightness of the extracted image are irregular. It is difficult to obtain accurate stream-line data through global binarization alone. Hence, the Niblack binarization algorithm is used to overcome this condition. Niblack binarization takes threshold values by region and then performs binarization. The Niblack binarization equation is shown below: (x, y) = m(x, y) + k s(x, y) (1) In this equation, m(x, y) and s(x, y) denote the average and standard deviation in a regional window, and k denotes the user defined variable for binarization. Niblack binarization determines the critical value using the average and standard deviation of pixel values inside the regional window, and the parts above the threshold value are extracted by comparing the critical value with the original image; these parts are then binarized. Appl. Sci. 2017, 7, 371 5 of 14 In this study, the deviation value of 0.2 was used in a window with 32 32 pixels. Because the parts detected on the image are stream lines with low temperatures, dark objects were extracted. After binarization, a mask method is used to delete data associated with the rod part, in which the seed is mounted, and the outer parts, which are not necessary for the measurement of stream lines on the surface of the melt. The region of interest is extracted from the original image using the mask method after binarization because the boundary line in the image may appear as data if binarization is Appl. Sci. 2017, 7, 371 5 of 14 performed after extraction. After After the regi the regionon of of inter interest i est issextracted, extracted, noi noise se ele elements, ments, excluding the stre excluding the str am lines on eam lines on the the melt surface, are removed using a low-pass filter and pixel size. Figure 4 shows the image melt surface, are removed using a low-pass filter and pixel size. Figure 4 shows the image preprocessing process. preprocessing process. (a) (b) (c) (d) (e) (f) Figure 4. Image preprocessing procedure: (a) acquiring image information; (b) extracting brightness Figure 4. Image preprocessing procedure: (a) acquiring image information; (b) extracting brightness information; (c) the binarization process; (d) extracting the region of interest; (e) removal of noise information; (c) the binarization process; (d) extracting the region of interest; (e) removal of noise elements; (f) result of image preprocessing. elements; (f) result of image preprocessing. The stream lines on the surface of the melt are distinguished through image preprocessing procedures. In the next image processing stage, the island position is traced using stream lines. The stream lines on the surface of the melt are distinguished through image preprocessing First, through the Hough transform of the acquired pixel data, a straight line representative of procedures. In the next image processing stage, the island position is traced using stream lines. each stream line is extracted. The Hough transform is illustrated in Figure 5. One point on the x-y coordinate system appears as one curve on the ρ-θ coordinate system, and one straight line on the x-y coordinate system is expressed as one point on the ρ-θ coordinate system. The region with the highest number of intersection points on the ρ-θ coordinate system is judged as a straight line in the x-y coordinate system, and a straight line in the image is detected in this way. Appl. Sci. 2017, 7, 371 6 of 14 First, through the Hough transform of the acquired pixel data, a straight line representative of each stream line is extracted. The Hough transform is illustrated in Figure 5. One point on the x-y coordinate system appears as one curve on the $- coordinate system, and one straight line on the x-y coordinate system is expressed as one point on the $- coordinate system. The region with the highest number of intersection points on the $- coordinate system is judged as a straight line in the x-y coordinate system, and a straight line in the image is detected in this way. Appl. Sci. 2017, 7, 371 6 of 14 Figure 5. Principle of Hough transform. Figure 5. Principle of Hough transform. Figure 6 shows the image processing procedure for detecting the position of stream–line intersection. Figure 6a shows an image that underwent the preprocessing procedure on the ρ-θ Figure 6 shows the image processing procedure for detecting the position of stream–line coordinate system. Figure 6b shows a histogram that represents the number of intersections of each intersection. Figure 6a shows an image that underwent the preprocessing procedure on the $- curve. The region with a large number of intersections of curves on the ρ-θ coordinate system is coordinate system. Figure 6b shows a histogram that represents the number of intersections of each actually expressed as a straight line on the x-y coordinate system. Thus, representative peak values curve. The region with a large number of intersections of curves on the $- coordinate system is actually are selected from each intersection region and moved to the x-y coordinate system region to generate expressed as a straight line on the x-y coordinate system. Thus, representative peak values are selected straight lines as shown in Figure 6c. In particular, because too many straight lines are likely to appear, from each intersection region and moved to the x-y coordinate system region to generate straight only points that have 100 or more intersections are transformed into straight lines, as shown in lines as shown in Figure 6c. In particular, because too many straight lines are likely to appear, only Figure 6c. Furthermore, the intersection count of curves on the ρ-θ coordinate system is assigned to points that have 100 or more intersections are transformed into straight lines, as shown in Figure 6c. each straight line as a weight value. Furthermor Fig e,uthe re 6d intersection shows the stage count in of which curves the coordin on the $-ates coor of the dinate islan system d center are d is assigned etected using to each t straight he straight line data from the x-y coordinate system acquired in the previous stage. To scan the entire line as a weight value. image, a circular region of 20 × 20 pixels is created on the x-y coordinate. At this time, the intersections Figure 6d shows the stage in which the coordinates of the island center are detected using the in the scanned region are detected and the sum of the weight values of the straight lines involved in straight line data from the x-y coordinate system acquired in the previous stage. To scan the entire the creation of each detected intersection is calculated. However, if the same straight line is included image, a circular region of 20 20 pixels is created on the x-y coordinate. At this time, the intersections twice when summing the weights of straight lines, it is calculated only once. The region that has the in the scanned region are detected and the sum of the weight values of the straight lines involved in the largest sum of intersection weights in the inspected image is detected and the center of the creation of each detected intersection is calculated. However, if the same straight line is included twice intersections in that region is calculated; this is represented as the coordinate of the island center in when summing the weights of straight lines, it is calculated only once. The region that has the largest the growth furnace melt material. Table 1 shows the parameter of image processing to find the center sum of intersection weights in the inspected image is detected and the center of the intersections in that point of the spoke pattern. region is calculated; this is represented as the coordinate of the island center in the growth furnace melt Table 1. Parameter of image processing. material. Table 1 shows the parameter of image processing to find the center point of the spoke pattern. Image Porcessing Parameters Value Table 1. Parameter of image processing. Color Plane Extraction HSL (Luminance Plane) Local threshold : Kernel size 32 × 32 (Niblack) : Deviation factor 0.20 Image Porcessing Parameters Value Image : Filter size 3 × 3 Color Plane Extraction HSL (Luminance Plane) Low-pass filter binarization : tolerance 50% : Kernel size 32 32 : Iterations 1 Local threshold (Niblack) : Deviation factor 0.20 Removal small objects : Pixel frame shape Square frame (3 pixel × 3 pixel) Image binarization : Filter size 3 3 : Connectivity 4 (Horizon or vertically adjacent) Low-pass filter : tolerance 50% Threshold 100 Hough transform : Iterations 20 × 20 (Circula 1 r) Removal small objects : Pixel frame shape Square frame (3 pixel 3 pixel) : Connectivity 4 (Horizon or vertically adjacent) Threshold 100 Hough transform 20 20 (Circular) Appl. Sci. 2017, 7, 371 7 of 14 Appl. Sci. 2017, 7, 371 7 of 14 (a) (b) (c) (d) (e) Figure 6. Image processing algorithm for detecting the island center point: (a) Hough transform; Figure 6. Image processing algorithm for detecting the island center point: (a) Hough transform; (b) intersection histogram; (c) straight line generation on x-y; (d) detection of central point; (e) detection (b) intersection histogram; (c) straight line generation on x-y; (d) detection of central point; (e) detection of island center point from the original image. of island center point from the original image. 3. Experiment Method and System Composition 3. Experiment Method and System Composition In the experiment, 32 kg grade sapphire growth equipment (Insight 200, ASTEK, Jeonnam, Korea) In the experiment, 32 kg grade sapphire growth equipment (Insight 200, ASTEK, Jeonnam, Korea) was used, and the crucible diameter is 200 mm. Figure 7 shows the single crystal growth of sapphire was used, and the crucible diameter is 200 mm. Figure 7 shows the single crystal growth of sapphire using the Ky method, as well as a sapphire ingot. In this study, the Ky method was used to measure using the Ky method, as well as a sapphire ingot. In this study, the Ky method was used to measure the surface of the melt in the seed preparation stage of sapphire growth. The experimental system the was surface set up of asthe shown melt in in Fig the ure seed 8. Th pris eparation system cons stage istsof of t sapphir he follow e gr ing: owth. a vision The experimental module unit, which system was consist set up s oas f a shown CCD cam in Figur era toe t8 a.ke p This hot system ographconsists s of the m ofethe lt mfollowing: aterial surfa ace and vision a fr module ame gr unit, abbe which r to convert the CCD camera images into processable signals; the shutter module unit, which has a shutter consists of a CCD camera to take photographs of the melt material surface and a frame grabber to and a pneumatic actuator for damage prevention; and the motion module unit, which measures the convert the CCD camera images into processable signals; the shutter module unit, which has a shutter location of the seed and moves the seed up and down for seeding. Appl. Sci. 2017, 7, 371 8 of 14 Appl. Sci. 2017, 7, 371 8 of 14 The CCD camera used in this experiment (model UI-6230SE, IDS, Obersulm, Germany) has a The CCD camera used in this experiment (model UI-6230SE, IDS, Obersulm, Germany) has a resolution of 1024 × 768 pixels and an image capture speed of 40.0 fps. The amount of light that enters resolution of 1024 × 768 pixels and an image capture speed of 40.0 fps. The amount of light that enters the camera was reduced using a neutral density filter (ND filter) because intense light is generated the camera was reduced using a neutral density filter (ND filter) because intense light is generated inside the growth furnace at 2000 °C or higher after alumina melting and before temperature inside the growth furnace at 2000 °C or higher after alumina melting and before temperature stabilization. The collected image signals are processed through an algorithm in a controlled PC and Appl. Sci. 2017, 7, 371 8 of 14 stabilization. The collected image signals are processed through an algorithm in a controlled PC and the results are analyzed to detect the central point. Shutter open/close control signals and rod the results are analyzed to detect the central point. Shutter open/close control signals and rod up-and-down movement commands are issued through the PC. up-and-down movement commands are issued through the PC. and a pneumatic actuator for damage prevention; and the motion module unit, which measures the The experiment progressed as shown in Figure 9. The camera shutter open/close cycle is 5 s, The experiment progressed as shown in Figure 9. The camera shutter open/close cycle is 5 s, location of the seed and moves the seed up and down for seeding. and the total cycle takes just one minute, during which the surface images are collected and analyzed. and the total cycle takes just one minute, during which the surface images are collected and analyzed. Figure 7. The single crystal growth of sapphire using the Ky method. Figure 7. The single crystal growth of sapphire using the Ky method. Figure 7. The single crystal growth of sapphire using the Ky method. Figure 8. Block diagram of the auto seeding system. Figure 8. Block diagram of the auto seeding system. Figure 8. Block diagram of the auto seeding system. The CCD camera used in this experiment (model UI-6230SE, IDS, Obersulm, Germany) has a resolution of 1024 768 pixels and an image capture speed of 40.0 fps. The amount of light that enters the camera was reduced using a neutral density filter (ND filter) because intense light is generated inside the growth furnace at 2000 C or higher after alumina melting and before temperature stabilization. The collected image signals are processed through an algorithm in a controlled PC and the results are analyzed to detect the central point. Shutter open/close control signals and rod up-and-down movement commands are issued through the PC. The experiment progressed as shown in Figure 9. The camera shutter open/close cycle is 5 s, and the total cycle takes just one minute, during which the surface images are collected and analyzed. Appl. Sci. 2017, 7, 371 9 of 14 Appl. Sci. 2017, 7, 371 9 of 14 Figure 9. Flowchart of the auto-seeding process. Figure 9. Flowchart of the auto-seeding process. 4. Results 4. Results During sapphire growth in the Ky method, images were acquired from the melt surface of the During sapphire growth in the Ky method, images were acquired from the melt surface of the alumina material using the proposed system, and the coordinates of the stream-line center were alumina material using the proposed system, and the coordinates of the stream-line center were processed through an image processing algorithm. After the melt material caused convection and processed through an image processing algorithm. After the melt material caused convection and before it was stabilized, 10 images were acquired at 5 min intervals. Figure 10 shows four befor representativ e it was stabilized, e images10 am images ong the 10 images were acquired that re at 5 min sulted intervals. from the ca Figur lcu el10 atio shows n using four the repr algorit esentative hm. images To determine among the re the 10liabilit images y of th thate rimag esulted e processing from the results, the is calculation u land position sing the algorithm. s (shown by To determine a green triangle) were compared with the central point positions of stream lines detected through the the reliability of the image processing results, the island positions (shown by a green triangle) were developed algorithm (shown by a red circle) in the figures. compared with the central point positions of stream lines detected through the developed algorithm The black regions obtained from image preprocessing represent the data of stream-line areas, (shown by a red circle) in the figures. which had low temperatures in the original image. Noise elements are generated in certain regions The black regions obtained from image preprocessing represent the data of stream-line areas, (see Figure 11) from the stream-line, depicted as black dots. This is one problem associated with which had low temperatures in the original image. Noise elements are generated in certain regions image binarization. These noise elements are generated from the results of binarization using regional (see Figure 11) from the stream-line, depicted as black dots. This is one problem associated with threshold values. Most noise can be removed by a low-pass filter and by removing pixels under image binarization. These noise elements are generated from the results of binarization using regional a certain pixel value. However, large black regions are not removed, as shown in the Figure 11. When threshold values. Most noise can be removed by a low-pass filter and by removing pixels under a these noise elements are calculated together with other stream-line elements in the Hough transform, certain pixel value. However, large black regions are not removed, as shown in the Figure 11. When unintended straight lines may be generated, causing a problem in the calculation of intersection these noise elements are calculated together with other stream-line elements in the Hough transform, points. Yet such unremoved shades are not a serious concern when detecting the center point of the unintended straight lines may be generated, causing a problem in the calculation of intersection points. island because their intersection count is smaller than that of the actual stream-line elements. Also, Yet such unremoved shades are not a serious concern when detecting the center point of the island they do not appear as main straight lines on the ρ-θ coordinate system. because their intersection count is smaller than that of the actual stream-line elements. Also, they do When the observed and detected islands were compared, in most cases, the coordinates of two not appear as main straight lines on the $- coordinate system. points were on the main stream lines, but coordinate errors occurred according to the shape of the When the observed and detected islands were compared, in most cases, the coordinates of two stream-line intersection positions. When stream lines intersected in the shape of a straight line, there points were on the main stream lines, but coordinate errors occurred according to the shape of the was about a 7-pixel (approximately 0.7 mm) error for the island coordinates. However, when the strstream eam-line line intersection s intersected in positions. a vortex sh When apstr e, as sh eam lines own in Figure intersected 12, in a ma the ximu shape m of error of 13.93 pixels a straight line, there was (approxim about a at 7-pixel ely 1.4 mm) oc (approximately curred in t 0.7 he mm) distanc err e be or tw for een the the island actually ob coordinates. served coordin However ates , an when d the the coordinates obtained from the algorithm. stream lines intersected in a vortex shape, as shown in Figure 12, a maximum error of 13.93 pixels (approximately 1.4 mm) occurred in the distance between the actually observed coordinates and the coordinates obtained from the algorithm. Appl. Sci. 2017, 7, 371 10 of 14 Appl. Sci. 2017, 7, 371 10 of 14 (a) (b) (c) (d) Figure 10. Image processing results of the melt surface. (Red: island position, Green: position of image Figure 10. Image processing results of the melt surface. (Red: island position, Green: position of image processing result). processing result). Appl. Sci. 2017, 7, 371 11 of 14 Appl. Sci. 2017, 7, 371 11 of 14 Appl. Sci. 2017, 7, 371 11 of 14 Figure 11. Noise elements after image preprocessing. Figure 11. Noise elements after image preprocessing. Figure 11. Noise elements after image preprocessing. Figure 12. Error detection at the curved parts of stream lines. (Red: island position, green: position of Figure 12. Error detection at the curved parts of stream lines. (Red: island position, green: position of image processing result). Figure 12. Error detection at the curved parts of stream lines. (Red: island position, green: position of image processing result). image processing result). Ten images were acquired from the time the surface began to melt inside the growth furnace. Ten images were acquired from the time the surface began to melt inside the growth furnace. The positions for the center point of the island as observed and as detected through the image Ten images were acquired from the time the surface began to melt inside the growth furnace. The positions for the center point of the island as observed and as detected through the image processing algorithm are shown in Figure 13. Table 2 shows the coordinates of the island as observed The positions for the center point of the island as observed and as detected through the image processing algorithm are shown in Figure 13. Table 2 shows the coordinates of the island as observed and as detected from the algorithm, as well as the distance between the coordinates in pixels. processing algorithm are shown in Figure 13. Table 2 shows the coordinates of the island as observed and as detected from the algorithm, as well as the distance between the coordinates in pixels. The difference ranged from 6.32 pixels (at the minimum) to 34 pixels (maximum), and the average and as detected from the algorithm, as well as the distance between the coordinates in pixels. The difference ranged from 6.32 pixels (at the minimum) to 34 pixels (maximum), and the average difference was 18.76 pixels, which is about 1.9 mm in actual distance. One explanation for these The difference ranged from 6.32 pixels (at the minimum) to 34 pixels (maximum), and the average difference was 18.76 pixels, which is about 1.9 mm in actual distance. One explanation for these differences between the two coordinate values is because the stream-line information at the center of difference was 18.76 pixels, which is about 1.9 mm in actual distance. One explanation for these differences between the two coordinate values is because the stream-line information at the center of the image was lost: about 25% of the region visible through the view point is occupied by the shape differences between the two coordinate values is because the stream-line information at the center of the image was lost: about 25% of the region visible through the view point is occupied by the shape of the rod and seed, as shown in Figure 14, resulting in lower accuracy. the image was lost: about 25% of the region visible through the view point is occupied by the shape of of the rod and seed, as shown in Figure 14, resulting in lower accuracy. In general, the internal convection of the melt material was stabilized after about 20 h in the the rod and seed, as shown in Figure 14, resulting in lower accuracy. In general, the internal convection of the melt material was stabilized after about 20 h in the heating process, and the center position of the island in the melt material is within 10 mm of the heating process, and the center position of the island in the melt material is within 10 mm of the center of the crucible in the growth furnace. After that, when the island size grows to 10–20 mm, center of the crucible in the growth furnace. After that, when the island size grows to 10–20 mm, the seed is lowered. Figure 15 displays the distance between the center of the island observed by the the seed is lowered. Figure 15 displays the distance between the center of the island observed by the naked eye and the center of the island detected from the algorithm taken from images acquired naked eye and the center of the island detected from the algorithm taken from images acquired during sapphire growth at the center position (750, 600) of the growth furnace crucible. All the island during sapphire growth at the center position (750, 600) of the growth furnace crucible. All the island centers were within 10 mm of the center of the growth furnace crucible. They approached the center centers were within 10 mm of the center of the growth furnace crucible. They approached the center of the crucible over time and continuously moved at distances of 2–3 mm. of the crucible over time and continuously moved at distances of 2–3 mm. Appl. Sci. 2017, 7, 371 12 of 14 Appl. Sci. 2017, 7, 371 12 of 14 Appl. Sci. 2017, 7, 371 12 of 14 (a) (b) (a) (b) Figure 13. Changes of island positions at the center of the growth furnace crucible: (a) observed Figure 13. Changes of island positions at the center of the growth furnace crucible: (a) observed Figure 13. Changes of island positions at the center of the growth furnace crucible: (a) observed positions; (b) the positions resulting from image processing. positions; (b) the positions resulting from image processing. positions; (b) the positions resulting from image processing. Table 2. Comparison of coordinates between observed and measured positions (i.e., the results of Table 2. Comparison of coordinates between observed and measured positions (i.e., the results of Tableimage processi 2. Comparison ng). of coordinates between observed and measured positions (i.e., the results of image processing). image processing). Distance between the Distance between the Observed Measured Positions Observed Measured Positions No. Coordinates/Positions No. Coordinates/Positions Positions (Image Proc Measuredessing Positions Results) Distance between the Positions (Image Processing Results) (in Pixels) No. Observed Positions (in Pixels) (Image Processing Results) Coordinates/Positions (in Pixels) 1 (647, 555) (653, 553) 6.32 1 (647, 555) (653, 553) 6.32 1 (647, 555) (653, 553) 6.32 2 2 (663 (663 , 57 , 57 6) 6) ( (679 679, 60 , 606) 6) 343 .00 4.00 2 (663, 576) (679, 606) 34.00 3 (680, 585) (701, 606) 29.70 3 (680, 585) (701, 606) 29.70 3 (680, 585) (701, 606) 29.70 4 (694, 569) (702, 576) 10.63 4 (694, 569) (702, 576) 10.63 4 (694, 569) (702, 576) 10.63 5 (713, 583) (698, 556) 30.89 5 (713, 583) (698, 556) 30.89 5 (713, 583) (698, 556) 30.89 6 (717, 563) (717, 556) 7.00 6 (717, 563) (717, 556) 7.00 6 (717, 563) (717, 556) 7.00 7 (733, 571) (728, 558) 25.94 7 (733, 571) (728, 558) 25.94 7 (733, 571) (728, 558) 25.94 8 (738, 573) (731, 573) 7.00 8 (738, 573) (731, 573) 7.00 8 (738, 573) (731, 573) 7.00 9 (742, 591) (745, 613) 22.20 9 (742, 591) (745, 613) 22.20 10 (728, 611) (741, 616) 13.93 9 (742, 591) (745, 613) 22.20 10 (728, 611) (741, 616) 13.93 10 (728, 611) (741, 616) 13.93 Figure 14. Loss of stream-line data caused by the outline of the rod and seed. Figure 14. Loss of stream-line data caused by the outline of the rod and seed. Figure 14. Loss of stream-line data caused by the outline of the rod and seed. In general, the internal convection of the melt material was stabilized after about 20 h in the heating process, and the center position of the island in the melt material is within 10 mm of the center of the crucible in the growth furnace. After that, when the island size grows to 10–20 mm, the seed is lowered. Figure 15 displays the distance between the center of the island observed by the naked eye and the center of the island detected from the algorithm taken from images acquired during sapphire growth at the center position (750, 600) of the growth furnace crucible. All the island centers were within 10 mm of the center of the growth furnace crucible. They approached the center of the crucible over time and continuously moved at distances of 2–3 mm. Appl. Sci. 2017, 7, 371 13 of 14 Appl. Sci. 2017, 7, 371 13 of 14 Figure 15. Distance from the center of the crucible in the growth furnace to the island center point. Figure 15. Distance from the center of the crucible in the growth furnace to the island center point. 5. Conclusions 5. Conclusions In this study, an alumina melt material surface measuring system was constructed that utilized a vision system for auto seeding in a single crystal sapphire ingot growth furnace using the Ky In this study, an alumina melt material surface measuring system was constructed that utilized method. Furthermore, the island position of the melt material was detected using the developed a vision system for auto seeding in a single crystal sapphire ingot growth furnace using the Ky image processing algorithm, and its performance was evaluated. method. Furthermore, the island position of the melt material was detected using the developed image To periodically acquire images of the alumina melt material surface during sapphire growth, an processing experimental system w algorithm, and its as performance constructed with a v was evaluated. ision module unit that contained a CCD camera, a shutter module unit to protect the camera, a motion module unit for the seeding process, and the PC To periodically acquire images of the alumina melt material surface during sapphire growth, control unit for total system control. The images were stably acquired through this system. an experimental system was constructed with a vision module unit that contained a CCD camera, a The stream lines and background regions on the alumina melt material surface were detected shutter module unit to protect the camera, a motion module unit for the seeding process, and the PC separately using an image-processing algorithm, and the island center coordinates were calculated control unit for total system control. The images were stably acquired through this system. using the intersections of the stream lines. The stream lines and background regions on the alumina melt material surface were detected When the observed coordinates of the island center were compared with the coordinates separately detected fro using an m the image image-pr processin ocessing g algorithm, ther algorithm, and e was the an ave island rage er center ror racoor te of 1. dinates 87 mm (wer based on e calculated 1024 × 768 pixels). The reason for this was that the accuracy level dropped for observed coordinates using the intersections of the stream lines. compared to algorithmically detected coordinates when some stream lines intersected in a vortex When the observed coordinates of the island center were compared with the coordinates shape and when the detection of stream lines was limited due to the outline of rod and seed shapes detected from the image processing algorithm, there was an average error rate of 1.87 mm (based on in the visual field. However, since the maximum error was about 3 mm when converted to actual 1024 768 pixels). The reason for this was that the accuracy level dropped for observed coordinates distance, this error did not cause a significant problem in judging the stable condition of the internal compar convecti ed to alg on of the mel orithmically t materia detected l during coor the actual gro dinates when wth furn some ace process. stream lines intersected in a vortex Considering the above results, the vision system and algorithm could be applied to the heater shape and when the detection of stream lines was limited due to the outline of rod and seed shapes type growth method of axial symmetry in a circular cylindrical crucible using the Cz method as well in the visual field. However, since the maximum error was about 3 mm when converted to actual as the Ky method. When applied to an actual system, these results can provide objective indicators distance, this error did not cause a significant problem in judging the stable condition of the internal for the seeding preparation process during sapphire growth. If the problems discovered in this study convection of the melt material during the actual growth furnace process. are addressed, such as the limited field of view and the error occurring in the curved parts of stream Considering the above results, the vision system and algorithm could be applied to the heater lines, automated techniques for the seeding process as well as the entire process of single crystal type growth sapphir method e growthof will axial be psymmetry ossible. in a circular cylindrical crucible using the Cz method as well as the Ky method. When applied to an actual system, these results can provide objective indicators for Author Contributions: C.M.K. and S.R.K. conceived and designed the experiments; C.M.K. performed the experiments; C.M.K., S.R.K. and J.H.A. analyzed the data; C.M.K. wrote the paper. the seeding preparation process during sapphire growth. If the problems discovered in this study are addressed, such as the limited field of view and the error occurring in the curved parts of stream lines, Conflicts of Interest: The authors declare no conflict of interest. automated techniques for the seeding process as well as the entire process of single crystal sapphire growth will be possible. Author Contributions: C.M.K. and S.R.K. conceived and designed the experiments; C.M.K. performed the experiments; C.M.K., S.R.K. and J.H.A. analyzed the data; C.M.K. wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Appl. Sci. 2017, 7, 371 14 of 14 References 1. Tang, H.; Li, H.; Xu, J. 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Available online: http://str-soft.com/products/CGSim/Kyropoulos_Sapphire/index.htm (accessed on 16 March 2017). © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Applied Sciences – Multidisciplinary Digital Publishing Institute
Published: Apr 7, 2017
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